5.3 Instruction_Finetuning.ipynb

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    "# Instruction Finetuning\n",
    "A varient of finetuning that enables GPT-3 to turn into ChatGPT and give it its chatting powers. There are all sorts of other tasks that we can do like reasoning, routing, copilot which is writing code, chat, different agents, but specifically instruction finetuning teaches the model to follow instructions and behave more like a chatbot. And this is a better user interface to interact with the model as we have seen with chatGPT. This is the method that turned GPT-3 into chatGPT which dramatically increased AI adoption from just a few reasearchers.\n",
    "\n",
    "## Instruction Following Dataset\n",
    "For this we can use data readily available either online or specific to our company and that might be FAQs customet support conversations, or Slack messages. We can also convert our data into an instruction following format or QnA format using prompt templates, we can also use another LLM to do this. There is a technique called **\"Alpaca\"** that uses chatGPT to do this.\n",
    "\n",
    "## Instruction Finetuning Generalization\n",
    "Instruction finetuning teaches a new behaviour to the model. We can generalize the idea of QnA to data we might not have given the model for the finetuning dataset. but the model had already learned in its pre-training step. Due to this chatGPT is able to give code in response even though it has not been trianed on coding QnA pairs."
   ]
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   "execution_count": 1,
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   "source": [
    "import itertools\n",
    "import jsonlines\n",
    "\n",
    "from datasets import load_dataset\n",
    "from pprint import pprint\n",
    "\n",
    "from llama import BasicModelRunner\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load instruction tuned dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "height": 47
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   "outputs": [],
   "source": [
    "instruction_tuned_dataset = load_dataset(\"tatsu-lab/alpaca\", split=\"train\", streaming=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "text": [
      "Instruction-tuned dataset:\n",
      "{'instruction': 'Give three tips for staying healthy.', 'input': '', 'output': '1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \\n2. Exercise regularly to keep your body active and strong. \\n3. Get enough sleep and maintain a consistent sleep schedule.', 'text': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nGive three tips for staying healthy.\\n\\n### Response:\\n1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \\n2. Exercise regularly to keep your body active and strong. \\n3. Get enough sleep and maintain a consistent sleep schedule.'}\n",
      "{'instruction': 'What are the three primary colors?', 'input': '', 'output': 'The three primary colors are red, blue, and yellow.', 'text': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nWhat are the three primary colors?\\n\\n### Response:\\nThe three primary colors are red, blue, and yellow.'}\n",
      "{'instruction': 'Describe the structure of an atom.', 'input': '', 'output': 'An atom is made up of a nucleus, which contains protons and neutrons, surrounded by electrons that travel in orbits around the nucleus. The protons and neutrons have a positive charge, while the electrons have a negative charge, resulting in an overall neutral atom. The number of each particle determines the atomic number and the type of atom.', 'text': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nDescribe the structure of an atom.\\n\\n### Response:\\nAn atom is made up of a nucleus, which contains protons and neutrons, surrounded by electrons that travel in orbits around the nucleus. The protons and neutrons have a positive charge, while the electrons have a negative charge, resulting in an overall neutral atom. The number of each particle determines the atomic number and the type of atom.'}\n",
      "{'instruction': 'How can we reduce air pollution?', 'input': '', 'output': 'There are a number of ways to reduce air pollution, such as shifting to renewable energy sources, encouraging the use of public transportation, prohibiting the burning of fossil fuels, implementing policies to reduce emissions from industrial sources, and implementing vehicle emissions standards. Additionally, individuals can do their part to reduce air pollution by reducing car use, avoiding burning materials such as wood, and changing to energy efficient appliances.', 'text': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nHow can we reduce air pollution?\\n\\n### Response:\\nThere are a number of ways to reduce air pollution, such as shifting to renewable energy sources, encouraging the use of public transportation, prohibiting the burning of fossil fuels, implementing policies to reduce emissions from industrial sources, and implementing vehicle emissions standards. Additionally, individuals can do their part to reduce air pollution by reducing car use, avoiding burning materials such as wood, and changing to energy efficient appliances.'}\n",
      "{'instruction': 'Describe a time when you had to make a difficult decision.', 'input': '', 'output': 'I had to make a difficult decision when I was working as a project manager at a construction company. I was in charge of a project that needed to be completed by a certain date in order to meet the client’s expectations. However, due to unexpected delays, we were not able to meet the deadline and so I had to make a difficult decision. I decided to extend the deadline, but I had to stretch the team’s resources even further and increase the budget. Although it was a risky decision, I ultimately decided to go ahead with it to ensure that the project was completed on time and that the client’s expectations were met. The project was eventually successfully completed and this was seen as a testament to my leadership and decision-making abilities.', 'text': 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nDescribe a time when you had to make a difficult decision.\\n\\n### Response:\\nI had to make a difficult decision when I was working as a project manager at a construction company. I was in charge of a project that needed to be completed by a certain date in order to meet the client’s expectations. However, due to unexpected delays, we were not able to meet the deadline and so I had to make a difficult decision. I decided to extend the deadline, but I had to stretch the team’s resources even further and increase the budget. Although it was a risky decision, I ultimately decided to go ahead with it to ensure that the project was completed on time and that the client’s expectations were met. The project was eventually successfully completed and this was seen as a testament to my leadership and decision-making abilities.'}\n"
     ]
    }
   ],
   "source": [
    "m = 5\n",
    "print(\"Instruction-tuned dataset:\")\n",
    "top_m = list(itertools.islice(instruction_tuned_dataset, m))\n",
    "for j in top_m:\n",
    "  print(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two prompt templates\n",
    "The authors of Alpaca paper had two prompt templates as they wanted the model to be able to work with two different types of promtps and two different types of tasks essentially and so one is instruction following one nad other one is without input."
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "prompt_template_with_input = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{instruction}\n",
    "\n",
    "### Input:\n",
    "{input}\n",
    "\n",
    "### Response:\"\"\"\n",
    "\n",
    "prompt_template_without_input = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{instruction}\n",
    "\n",
    "### Response:\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hydrate prompts (add data to prompts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "height": 183
   },
   "outputs": [],
   "source": [
    "processed_data = []\n",
    "for j in top_m:\n",
    "  if not j[\"input\"]:\n",
    "    processed_prompt = prompt_template_without_input.format(instruction=j[\"instruction\"])\n",
    "  else:\n",
    "    processed_prompt = prompt_template_with_input.format(instruction=j[\"instruction\"], input=j[\"input\"])\n",
    "\n",
    "  processed_data.append({\"input\": processed_prompt, \"output\": j[\"output\"]})\n"
   ]
  },
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   "execution_count": 6,
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     "text": [
      "{'input': 'Below is an instruction that describes a task. Write a response '\n",
      "          'that appropriately completes the request.\\n'\n",
      "          '\\n'\n",
      "          '### Instruction:\\n'\n",
      "          'Give three tips for staying healthy.\\n'\n",
      "          '\\n'\n",
      "          '### Response:',\n",
      " 'output': '1.Eat a balanced diet and make sure to include plenty of fruits '\n",
      "           'and vegetables. \\n'\n",
      "           '2. Exercise regularly to keep your body active and strong. \\n'\n",
      "           '3. Get enough sleep and maintain a consistent sleep schedule.'}\n"
     ]
    }
   ],
   "source": [
    "pprint(processed_data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save data to jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "with jsonlines.open(f'alpaca_processed.jsonl', 'w') as writer:\n",
    "    writer.write_all(processed_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "height": 64
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['input', 'output'],\n",
      "        num_rows: 52002\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "dataset_path_hf = \"alpaca\"\n",
    "dataset_hf = load_dataset(dataset_path_hf)\n",
    "print(dataset_hf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instruction-tuned output (ChatGPT) responds with:\n",
    "\n",
    "> Training your dog to sit is a basic and essential command that can be taught using positive reinforcement. Here's a simple step-by-step guide:\n",
    "> 1. **Prepare Treats:**\n",
    "   Gather small, soft treats that your dog enjoys. Make sure they are easy to chew and won't take too long to eat.\n",
    "> 2. **Find a Quiet Space:**\n",
    "   Choose a quiet area with minimal distractions for the training session. This will help your dog focus better.\n",
    "> 3. **Get Your Dog's Attention:**\n",
    "   Call your dog's name to get their attention. Make sure they are looking at you.\n",
    "> 4. **Use a Treat to Lure:**\n",
    "   Hold a treat close to your dog's nose, and slowly move your hand upward and slightly backward over their head. As you do this, your dog's natural response will be to follow the treat with their nose, causing them to sit.\n",
    "> 5. **Say the Command:**\n",
    "   As your dog starts to sit, say the command \"Sit\" in a clear and firm voice. Use the word consistently every time you want your dog to sit.\n",
    "> 6. **Reward and Praise:**\n",
    "   As soon as your dog sits, immediately reward them with the treat and offer verbal praise. This positive reinforcement will help them associate sitting with positive outcomes.\n",
    "> 7. **Repeat and Practice:**\n",
    "   Repeat the process several times in a row during each training session. Keep the sessions short (around 5-10 minutes) to prevent your dog from losing interest.\n",
    "> 8. **Add Duration:**\n",
    "   Once your dog consistently sits on command, you can gradually increase the duration by waiting a couple of seconds before giving the treat. This helps reinforce the sit command.\n",
    "> 9. **Generalize the Command:**\n",
    "   Practice the \"sit\" command in different locations and with various distractions to help your dog generalize the behavior.\n",
    "> 10. **Be Patient and Consistent:**\n",
    "    Patience and consistency are key in dog training. Always use positive reinforcement, and avoid punishment. If your dog doesn't succeed initially, go back a step and try again.\n",
    "> \n",
    "> Remember that each dog is unique, and some may learn more quickly than others. Adjust your training approach based on your dog's individual needs and progress."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try smaller models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/pythia-70m\")\n",
    "model = AutoModelForCausalLM.from_pretrained(\"EleutherAI/pythia-70m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "height": 421
   },
   "outputs": [],
   "source": [
    "def inference(text, model, tokenizer, max_input_tokens=1000, max_output_tokens=100):\n",
    "  # Tokenize\n",
    "  input_ids = tokenizer.encode(\n",
    "          text,\n",
    "          return_tensors=\"pt\",\n",
    "          truncation=True,\n",
    "          max_length=max_input_tokens\n",
    "  )\n",
    "\n",
    "  # Generate\n",
    "  device = model.device\n",
    "  generated_tokens_with_prompt = model.generate(\n",
    "    input_ids=input_ids.to(device),\n",
    "    max_length=max_output_tokens\n",
    "  )\n",
    "\n",
    "  # Decode\n",
    "  generated_text_with_prompt = tokenizer.batch_decode(generated_tokens_with_prompt, skip_special_tokens=True)\n",
    "\n",
    "  # Strip the prompt\n",
    "  generated_text_answer = generated_text_with_prompt[0][len(text):]\n",
    "\n",
    "  return generated_text_answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "height": 64
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['question', 'answer', 'input_ids', 'attention_mask', 'labels'],\n",
      "        num_rows: 1260\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['question', 'answer', 'input_ids', 'attention_mask', 'labels'],\n",
      "        num_rows: 140\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "finetuning_dataset_path = \"lamini_docs\"\n",
    "finetuning_dataset = load_dataset(finetuning_dataset_path)\n",
    "print(finetuning_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "height": 81
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'question': 'Can Lamini generate technical documentation or user manuals for software projects?', 'answer': 'Yes, Lamini can generate technical documentation and user manuals for software projects. It uses natural language generation techniques to create clear and concise documentation that is easy to understand for both technical and non-technical users. This can save developers a significant amount of time and effort in creating documentation, allowing them to focus on other aspects of their projects.', 'input_ids': [5804, 418, 4988, 74, 6635, 7681, 10097, 390, 2608, 11595, 84, 323, 3694, 6493, 32, 4374, 13, 418, 4988, 74, 476, 6635, 7681, 10097, 285, 2608, 11595, 84, 323, 3694, 6493, 15, 733, 4648, 3626, 3448, 5978, 5609, 281, 2794, 2590, 285, 44003, 10097, 326, 310, 3477, 281, 2096, 323, 1097, 7681, 285, 1327, 14, 48746, 4212, 15, 831, 476, 5321, 12259, 247, 1534, 2408, 273, 673, 285, 3434, 275, 6153, 10097, 13, 6941, 731, 281, 2770, 327, 643, 7794, 273, 616, 6493, 15], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': [5804, 418, 4988, 74, 6635, 7681, 10097, 390, 2608, 11595, 84, 323, 3694, 6493, 32, 4374, 13, 418, 4988, 74, 476, 6635, 7681, 10097, 285, 2608, 11595, 84, 323, 3694, 6493, 15, 733, 4648, 3626, 3448, 5978, 5609, 281, 2794, 2590, 285, 44003, 10097, 326, 310, 3477, 281, 2096, 323, 1097, 7681, 285, 1327, 14, 48746, 4212, 15, 831, 476, 5321, 12259, 247, 1534, 2408, 273, 673, 285, 3434, 275, 6153, 10097, 13, 6941, 731, 281, 2770, 327, 643, 7794, 273, 616, 6493, 15]}\n",
      "\n",
      "\n",
      "I have a question about the following:\n",
      "\n",
      "How do I get the correct documentation to work?\n",
      "\n",
      "A:\n",
      "\n",
      "I think you need to use the following code:\n",
      "\n",
      "A:\n",
      "\n",
      "You can use the following code to get the correct documentation.\n",
      "\n",
      "A:\n",
      "\n",
      "You can use the following code to get the correct documentation.\n",
      "\n",
      "A:\n",
      "\n",
      "You can use the following\n"
     ]
    }
   ],
   "source": [
    "test_sample = finetuning_dataset[\"test\"][0]\n",
    "print(test_sample)\n",
    "\n",
    "print(inference(test_sample[\"question\"], model, tokenizer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare to finetuned small model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "instruction_model = AutoModelForCausalLM.from_pretrained(\"lamini_docs_finetuned\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes, Lamini can generate technical documentation or user manuals for software projects. This can be achieved by providing a prompt for a specific technical question or question to the LLM Engine, or by providing a prompt for a specific technical question or question. Additionally, Lamini can be trained on specific technical questions or questions to help users understand the process and provide feedback to the LLM Engine. Additionally, Lamini\n"
     ]
    }
   ],
   "source": [
    "print(inference(test_sample[\"question\"], instruction_model, tokenizer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "height": 234,
    "id": "mSJMi8I4Sgrw"
   },
   "outputs": [],
   "source": []
  }
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